Harvesting and visualising ‘big’ social media data is an increasingly feasible practice for social scientists. Yet whilst there is an emerging and substantial body of literature utilising social media as a data resource, there are a number of computational issues affecting data collection and analysis that for the large part remain hidden from the researcher’s view but which may problematise the findings we can legitimately draw from social media. This chapter outlines and explores two such issues as they occur for data taken from Twitter , commenting on how they might be handled in the undertaking of digital social science research. Here, we agree wholeheartedly subscribe to with Procter et al.’s insistence ‘that social researchers be trained in the underlying concepts of computational methods and tools so they can decide when and how to apply them’ (2013: 209). As such, we aim to outline highlight certain technical features and/or constraints pertaining to the collection and processing of Twitter data. In doing this we aim to, thereby helping help researchers to incorporate a technical understanding of the mechanics of digital research tools into robust and thoughtful analyses of their data.
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Brooker, P., Barnett, J., Cribbin, T., & Sharma, S. (2015). Have we even solved the first ‘big data challenge?’ Practical issues concerning data collection and visual representation for social media analytics. In H. Snee, C. Hine, Y. Morey, S. Roberts, & H. Watson (Eds.), Digital Methods for Social Science: An Interdisciplinary Guide to Research Innovation. Palgrave Macmillan. http://www.palgrave.com/page/detail/Digital-Methods-for-Social-Science/?sf1=barcode&st1=9781137453655 or e-copy on Google Books